Overview

Dataset statistics

Number of variables47
Number of observations60398
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.7 MiB
Average record size in memory376.0 B

Variable types

Numeric18
Categorical21
Text3
DateTime5

Alerts

RevisionNumber has constant value ""Constant
OrderQuantity has constant value ""Constant
Age is highly overall correlated with Age Range and 1 other fieldsHigh correlation
Age Range is highly overall correlated with AgeHigh correlation
Cost is highly overall correlated with DimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryName and 9 other fieldsHigh correlation
CurrencyKey is highly overall correlated with DimCurrency.CurrencyName and 4 other fieldsHigh correlation
DimCurrency.CurrencyName is highly overall correlated with CurrencyKey and 3 other fieldsHigh correlation
DimCustomer.EnglishOccupation is highly overall correlated with YearlyIncomeRangeHigh correlation
DimCustomer.TotalChildren is highly overall correlated with AgeHigh correlation
DimCustomer.YearlyIncome is highly overall correlated with YearlyIncomeRangeHigh correlation
DimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryName is highly overall correlated with Cost and 9 other fieldsHigh correlation
DimProduct.DimProductSubcategory.EnglishProductSubcategoryName is highly overall correlated with DimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryName and 1 other fieldsHigh correlation
DimProduct.ProductSubcategoryKey is highly overall correlated with Cost and 9 other fieldsHigh correlation
DimPromotion.EnglishPromotionCategory is highly overall correlated with DimPromotion.EnglishPromotionName and 2 other fieldsHigh correlation
DimPromotion.EnglishPromotionName is highly overall correlated with DimPromotion.EnglishPromotionCategory and 2 other fieldsHigh correlation
DimPromotion.EnglishPromotionType is highly overall correlated with DimPromotion.EnglishPromotionCategory and 2 other fieldsHigh correlation
DimSalesTerritory.SalesTerritoryCountry is highly overall correlated with CurrencyKey and 4 other fieldsHigh correlation
DimSalesTerritory.SalesTerritoryGroup is highly overall correlated with CurrencyKey and 3 other fieldsHigh correlation
DimSalesTerritory.SalesTerritoryRegion is highly overall correlated with CurrencyKey and 3 other fieldsHigh correlation
ExtendedAmount is highly overall correlated with Cost and 9 other fieldsHigh correlation
Freight is highly overall correlated with Cost and 9 other fieldsHigh correlation
ProductKey is highly overall correlated with DimProduct.DimProductSubcategory.EnglishProductSubcategoryNameHigh correlation
ProductStandardCost is highly overall correlated with Cost and 9 other fieldsHigh correlation
Profit is highly overall correlated with Cost and 8 other fieldsHigh correlation
PromotionKey is highly overall correlated with DimPromotion.EnglishPromotionCategory and 2 other fieldsHigh correlation
SalesAmount is highly overall correlated with Cost and 9 other fieldsHigh correlation
SalesTerritoryKey is highly overall correlated with CurrencyKey and 1 other fieldsHigh correlation
TaxAmt is highly overall correlated with Cost and 9 other fieldsHigh correlation
TotalProductCost is highly overall correlated with Cost and 9 other fieldsHigh correlation
UnitPrice is highly overall correlated with Cost and 9 other fieldsHigh correlation
YearlyIncomeRange is highly overall correlated with DimCustomer.EnglishOccupation and 1 other fieldsHigh correlation
PromotionKey is highly imbalanced (88.7%)Imbalance
DimPromotion.EnglishPromotionName is highly imbalanced (88.7%)Imbalance
DimPromotion.EnglishPromotionType is highly imbalanced (85.7%)Imbalance
DimPromotion.EnglishPromotionCategory is highly imbalanced (77.8%)Imbalance
DimCustomer.TotalChildren has 17048 (28.2%) zerosZeros

Reproduction

Analysis started2024-04-07 17:14:08.762962
Analysis finished2024-04-07 17:16:38.715919
Duration2 minutes and 29.95 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

ProductKey
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.55793
Minimum214
Maximum606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:38.869139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum214
5-th percentile217
Q1359
median479
Q3529
95-th percentile581
Maximum606
Range392
Interquartile range (IQR)170

Descriptive statistics

Standard deviation118.08839
Coefficient of variation (CV)0.26988058
Kurtosis-0.67398995
Mean437.55793
Median Absolute Deviation (MAD)58
Skewness-0.79879556
Sum26427624
Variance13944.868
MonotonicityNot monotonic
2024-04-07T17:16:39.116673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477 4244
 
7.0%
480 3191
 
5.3%
528 3095
 
5.1%
529 2376
 
3.9%
214 2230
 
3.7%
225 2190
 
3.6%
222 2125
 
3.5%
485 2121
 
3.5%
217 2085
 
3.5%
478 2025
 
3.4%
Other values (148) 34716
57.5%
ValueCountFrequency (%)
214 2230
3.7%
217 2085
3.5%
222 2125
3.5%
225 2190
3.6%
228 429
 
0.7%
231 442
 
0.7%
234 452
 
0.7%
237 413
 
0.7%
310 336
 
0.6%
311 281
 
0.5%
ValueCountFrequency (%)
606 386
0.6%
605 363
0.6%
604 360
0.6%
600 41
 
0.1%
599 56
 
0.1%
598 58
 
0.1%
597 49
 
0.1%
596 48
 
0.1%
595 48
 
0.1%
594 50
 
0.1%

CustomerKey
Real number (ℝ)

Distinct18484
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18841.685
Minimum11000
Maximum29483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:39.899564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11414
Q114003
median18143
Q323429.75
95-th percentile28108.15
Maximum29483
Range18483
Interquartile range (IQR)9426.75

Descriptive statistics

Standard deviation5432.4304
Coefficient of variation (CV)0.28831977
Kurtosis-1.1617483
Mean18841.685
Median Absolute Deviation (MAD)4594.5
Skewness0.28365312
Sum1.1380001 × 109
Variance29511300
MonotonicityNot monotonic
2024-04-07T17:16:40.132423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11185 68
 
0.1%
11300 67
 
0.1%
11277 65
 
0.1%
11262 63
 
0.1%
11287 62
 
0.1%
11176 60
 
0.1%
11091 59
 
0.1%
11331 58
 
0.1%
11566 58
 
0.1%
11330 57
 
0.1%
Other values (18474) 59781
99.0%
ValueCountFrequency (%)
11000 8
< 0.1%
11001 11
< 0.1%
11002 4
 
< 0.1%
11003 9
< 0.1%
11004 6
< 0.1%
11005 6
< 0.1%
11006 5
< 0.1%
11007 8
< 0.1%
11008 7
< 0.1%
11009 5
< 0.1%
ValueCountFrequency (%)
29483 1
 
< 0.1%
29482 1
 
< 0.1%
29481 1
 
< 0.1%
29480 5
< 0.1%
29479 1
 
< 0.1%
29478 3
< 0.1%
29477 3
< 0.1%
29476 1
 
< 0.1%
29475 1
 
< 0.1%
29474 1
 
< 0.1%

PromotionKey
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
1
58247 
2
 
2118
13
 
20
14
 
13

Length

Max length2
Median length1
Mean length1.0005464
Min length1

Characters and Unicode

Total characters60431
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 58247
96.4%
2 2118
 
3.5%
13 20
 
< 0.1%
14 13
 
< 0.1%

Length

2024-04-07T17:16:40.356394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:40.581309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 58247
96.4%
2 2118
 
3.5%
13 20
 
< 0.1%
14 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 58280
96.4%
2 2118
 
3.5%
3 20
 
< 0.1%
4 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 58280
96.4%
2 2118
 
3.5%
3 20
 
< 0.1%
4 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 58280
96.4%
2 2118
 
3.5%
3 20
 
< 0.1%
4 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 58280
96.4%
2 2118
 
3.5%
3 20
 
< 0.1%
4 13
 
< 0.1%

CurrencyKey
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.845326
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:40.754372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q119
median100
Q3100
95-th percentile100
Maximum100
Range94
Interquartile range (IQR)81

Descriptive statistics

Standard deviation42.146363
Coefficient of variation (CV)0.60342425
Kurtosis-1.4488281
Mean69.845326
Median Absolute Deviation (MAD)0
Skewness-0.72070268
Sum4218518
Variance1776.3159
MonotonicityNot monotonic
2024-04-07T17:16:40.928271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
100 33400
55.3%
6 12988
 
21.5%
19 7135
 
11.8%
98 6740
 
11.2%
29 76
 
0.1%
39 59
 
0.1%
ValueCountFrequency (%)
6 12988
 
21.5%
19 7135
 
11.8%
29 76
 
0.1%
39 59
 
0.1%
98 6740
 
11.2%
100 33400
55.3%
ValueCountFrequency (%)
100 33400
55.3%
98 6740
 
11.2%
39 59
 
0.1%
29 76
 
0.1%
19 7135
 
11.8%
6 12988
 
21.5%

SalesTerritoryKey
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2444617
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:41.180013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9611497
Coefficient of variation (CV)0.47420416
Kurtosis-0.99206078
Mean6.2444617
Median Absolute Deviation (MAD)2
Skewness-0.48527069
Sum377153
Variance8.7684077
MonotonicityNot monotonic
2024-04-07T17:16:41.461968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9 13345
22.1%
4 12265
20.3%
1 8993
14.9%
6 7620
12.6%
10 6906
11.4%
8 5625
9.3%
7 5558
9.2%
5 39
 
0.1%
2 27
 
< 0.1%
3 20
 
< 0.1%
ValueCountFrequency (%)
1 8993
14.9%
2 27
 
< 0.1%
3 20
 
< 0.1%
4 12265
20.3%
5 39
 
0.1%
6 7620
12.6%
7 5558
9.2%
8 5625
9.3%
9 13345
22.1%
10 6906
11.4%
ValueCountFrequency (%)
10 6906
11.4%
9 13345
22.1%
8 5625
9.3%
7 5558
9.2%
6 7620
12.6%
5 39
 
0.1%
4 12265
20.3%
3 20
 
< 0.1%
2 27
 
< 0.1%
1 8993
14.9%
Distinct27659
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:42.101207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters422786
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9668 ?
Unique (%)16.0%

Sample

1st rowSO43700
2nd rowSO43719
3rd rowSO43726
4th rowSO43762
5th rowSO43771
ValueCountFrequency (%)
so72656 8
 
< 0.1%
so58845 8
 
< 0.1%
so70714 8
 
< 0.1%
so58572 7
 
< 0.1%
so74869 7
 
< 0.1%
so71961 7
 
< 0.1%
so51555 7
 
< 0.1%
so61412 7
 
< 0.1%
so54784 7
 
< 0.1%
so64542 7
 
< 0.1%
Other values (27649) 60325
99.9%
2024-04-07T17:16:43.033536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 60398
14.3%
O 60398
14.3%
6 47143
11.2%
5 44004
10.4%
7 35344
8.4%
4 30836
7.3%
2 25066
5.9%
3 24928
5.9%
1 23946
 
5.7%
0 23824
 
5.6%
Other values (2) 46899
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 422786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 60398
14.3%
O 60398
14.3%
6 47143
11.2%
5 44004
10.4%
7 35344
8.4%
4 30836
7.3%
2 25066
5.9%
3 24928
5.9%
1 23946
 
5.7%
0 23824
 
5.6%
Other values (2) 46899
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 422786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 60398
14.3%
O 60398
14.3%
6 47143
11.2%
5 44004
10.4%
7 35344
8.4%
4 30836
7.3%
2 25066
5.9%
3 24928
5.9%
1 23946
 
5.7%
0 23824
 
5.6%
Other values (2) 46899
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 422786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 60398
14.3%
O 60398
14.3%
6 47143
11.2%
5 44004
10.4%
7 35344
8.4%
4 30836
7.3%
2 25066
5.9%
3 24928
5.9%
1 23946
 
5.7%
0 23824
 
5.6%
Other values (2) 46899
11.1%

SalesOrderLineNumber
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8863207
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:43.294717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0163284
Coefficient of variation (CV)0.53878876
Kurtosis0.69706876
Mean1.8863207
Median Absolute Deviation (MAD)1
Skewness1.0693116
Sum113930
Variance1.0329235
MonotonicityNot monotonic
2024-04-07T17:16:43.456065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 27659
45.8%
2 17991
29.8%
3 9828
 
16.3%
4 3958
 
6.6%
5 820
 
1.4%
6 124
 
0.2%
7 15
 
< 0.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
1 27659
45.8%
2 17991
29.8%
3 9828
 
16.3%
4 3958
 
6.6%
5 820
 
1.4%
6 124
 
0.2%
7 15
 
< 0.1%
8 3
 
< 0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 15
 
< 0.1%
6 124
 
0.2%
5 820
 
1.4%
4 3958
 
6.6%
3 9828
 
16.3%
2 17991
29.8%
1 27659
45.8%

RevisionNumber
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
1
60398 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 60398
100.0%

Length

2024-04-07T17:16:43.649102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:43.838085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 60398
100.0%

Most occurring characters

ValueCountFrequency (%)
1 60398
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

OrderQuantity
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
1
60398 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 60398
100.0%

Length

2024-04-07T17:16:43.983345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:44.160958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 60398
100.0%

Most occurring characters

ValueCountFrequency (%)
1 60398
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 60398
100.0%

UnitPrice
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.08691
Minimum2.29
Maximum3578.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:44.333647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile2.29
Q17.95
median29.99
Q3539.99
95-th percentile2443.35
Maximum3578.27
Range3575.98
Interquartile range (IQR)532.04

Descriptive statistics

Standard deviation928.48989
Coefficient of variation (CV)1.9101314
Kurtosis2.5136486
Mean486.08691
Median Absolute Deviation (MAD)25
Skewness1.9275149
Sum29358677
Variance862093.48
MonotonicityNot monotonic
2024-04-07T17:16:44.551134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.99 8827
 
14.6%
34.99 6440
 
10.7%
8.99 4470
 
7.4%
2.29 3191
 
5.3%
3.99 2376
 
3.9%
21.98 2121
 
3.5%
9.99 2025
 
3.4%
24.99 1788
 
3.0%
49.99 1736
 
2.9%
539.99 1695
 
2.8%
Other values (32) 25729
42.6%
ValueCountFrequency (%)
2.29 3191
 
5.3%
3.99 2376
 
3.9%
4.99 8827
14.6%
7.95 908
 
1.5%
8.99 4470
7.4%
9.99 2025
 
3.4%
21.49 1044
 
1.7%
21.98 2121
 
3.5%
24.49 1430
 
2.4%
24.99 1788
 
3.0%
ValueCountFrequency (%)
3578.27 1551
2.6%
3399.99 185
 
0.3%
3374.99 211
 
0.3%
2443.35 1145
1.9%
2384.07 1255
2.1%
2319.99 1215
2.0%
2294.99 1262
2.1%
2181.5625 758
1.3%
2071.4196 521
 
0.9%
2049.0982 554
 
0.9%

ExtendedAmount
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.08691
Minimum2.29
Maximum3578.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:44.789359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile2.29
Q17.95
median29.99
Q3539.99
95-th percentile2443.35
Maximum3578.27
Range3575.98
Interquartile range (IQR)532.04

Descriptive statistics

Standard deviation928.48989
Coefficient of variation (CV)1.9101314
Kurtosis2.5136486
Mean486.08691
Median Absolute Deviation (MAD)25
Skewness1.9275149
Sum29358677
Variance862093.48
MonotonicityNot monotonic
2024-04-07T17:16:45.007076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.99 8827
 
14.6%
34.99 6440
 
10.7%
8.99 4470
 
7.4%
2.29 3191
 
5.3%
3.99 2376
 
3.9%
21.98 2121
 
3.5%
9.99 2025
 
3.4%
24.99 1788
 
3.0%
49.99 1736
 
2.9%
539.99 1695
 
2.8%
Other values (32) 25729
42.6%
ValueCountFrequency (%)
2.29 3191
 
5.3%
3.99 2376
 
3.9%
4.99 8827
14.6%
7.95 908
 
1.5%
8.99 4470
7.4%
9.99 2025
 
3.4%
21.49 1044
 
1.7%
21.98 2121
 
3.5%
24.49 1430
 
2.4%
24.99 1788
 
3.0%
ValueCountFrequency (%)
3578.27 1551
2.6%
3399.99 185
 
0.3%
3374.99 211
 
0.3%
2443.35 1145
1.9%
2384.07 1255
2.1%
2319.99 1215
2.0%
2294.99 1262
2.1%
2181.5625 758
1.3%
2071.4196 521
 
0.9%
2049.0982 554
 
0.9%

ProductStandardCost
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.06566
Minimum0.8565
Maximum2171.2942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:45.240743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.8565
5-th percentile0.8565
Q12.9733
median11.2163
Q3294.5797
95-th percentile1518.7864
Maximum2171.2942
Range2170.4377
Interquartile range (IQR)291.6064

Descriptive statistics

Standard deviation552.45764
Coefficient of variation (CV)1.9312267
Kurtosis2.6883605
Mean286.06566
Median Absolute Deviation (MAD)9.35
Skewness1.9505468
Sum17277794
Variance305209.44
MonotonicityNot monotonic
2024-04-07T17:16:45.455272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.8663 8827
 
14.6%
13.0863 6440
 
10.7%
0.8565 3191
 
5.3%
1.4923 2376
 
3.9%
3.3623 2280
 
3.8%
6.9223 2190
 
3.6%
8.2205 2121
 
3.5%
3.7363 2025
 
3.4%
9.3463 1788
 
3.0%
38.4923 1736
 
2.9%
Other values (35) 27424
45.4%
ValueCountFrequency (%)
0.8565 3191
 
5.3%
1.4923 2376
 
3.9%
1.8663 8827
14.6%
2.9733 908
 
1.5%
3.3623 2280
 
3.8%
3.7363 2025
 
3.4%
6.9223 2190
 
3.6%
8.0373 1044
 
1.7%
8.2205 2121
 
3.5%
9.1593 1430
 
2.4%
ValueCountFrequency (%)
2171.2942 1551
2.6%
1912.1544 185
 
0.3%
1898.0944 211
 
0.3%
1554.9479 706
1.2%
1518.7864 439
 
0.7%
1481.9379 1255
2.1%
1320.6838 758
1.3%
1265.6195 1215
2.0%
1251.9813 1262
2.1%
1117.8559 521
 
0.9%

TotalProductCost
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.06566
Minimum0.8565
Maximum2171.2942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:45.678725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.8565
5-th percentile0.8565
Q12.9733
median11.2163
Q3294.5797
95-th percentile1518.7864
Maximum2171.2942
Range2170.4377
Interquartile range (IQR)291.6064

Descriptive statistics

Standard deviation552.45764
Coefficient of variation (CV)1.9312267
Kurtosis2.6883605
Mean286.06566
Median Absolute Deviation (MAD)9.35
Skewness1.9505468
Sum17277794
Variance305209.44
MonotonicityNot monotonic
2024-04-07T17:16:45.904723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.8663 8827
 
14.6%
13.0863 6440
 
10.7%
0.8565 3191
 
5.3%
1.4923 2376
 
3.9%
3.3623 2280
 
3.8%
6.9223 2190
 
3.6%
8.2205 2121
 
3.5%
3.7363 2025
 
3.4%
9.3463 1788
 
3.0%
38.4923 1736
 
2.9%
Other values (35) 27424
45.4%
ValueCountFrequency (%)
0.8565 3191
 
5.3%
1.4923 2376
 
3.9%
1.8663 8827
14.6%
2.9733 908
 
1.5%
3.3623 2280
 
3.8%
3.7363 2025
 
3.4%
6.9223 2190
 
3.6%
8.0373 1044
 
1.7%
8.2205 2121
 
3.5%
9.1593 1430
 
2.4%
ValueCountFrequency (%)
2171.2942 1551
2.6%
1912.1544 185
 
0.3%
1898.0944 211
 
0.3%
1554.9479 706
1.2%
1518.7864 439
 
0.7%
1481.9379 1255
2.1%
1320.6838 758
1.3%
1265.6195 1215
2.0%
1251.9813 1262
2.1%
1117.8559 521
 
0.9%

SalesAmount
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean486.08691
Minimum2.29
Maximum3578.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:46.132301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile2.29
Q17.95
median29.99
Q3539.99
95-th percentile2443.35
Maximum3578.27
Range3575.98
Interquartile range (IQR)532.04

Descriptive statistics

Standard deviation928.48989
Coefficient of variation (CV)1.9101314
Kurtosis2.5136486
Mean486.08691
Median Absolute Deviation (MAD)25
Skewness1.9275149
Sum29358677
Variance862093.48
MonotonicityNot monotonic
2024-04-07T17:16:46.347212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.99 8827
 
14.6%
34.99 6440
 
10.7%
8.99 4470
 
7.4%
2.29 3191
 
5.3%
3.99 2376
 
3.9%
21.98 2121
 
3.5%
9.99 2025
 
3.4%
24.99 1788
 
3.0%
49.99 1736
 
2.9%
539.99 1695
 
2.8%
Other values (32) 25729
42.6%
ValueCountFrequency (%)
2.29 3191
 
5.3%
3.99 2376
 
3.9%
4.99 8827
14.6%
7.95 908
 
1.5%
8.99 4470
7.4%
9.99 2025
 
3.4%
21.49 1044
 
1.7%
21.98 2121
 
3.5%
24.49 1430
 
2.4%
24.99 1788
 
3.0%
ValueCountFrequency (%)
3578.27 1551
2.6%
3399.99 185
 
0.3%
3374.99 211
 
0.3%
2443.35 1145
1.9%
2384.07 1255
2.1%
2319.99 1215
2.0%
2294.99 1262
2.1%
2181.5625 758
1.3%
2071.4196 521
 
0.9%
2049.0982 554
 
0.9%

TaxAmt
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.886954
Minimum0.1832
Maximum286.2616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:46.580180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1832
5-th percentile0.1832
Q10.636
median2.3992
Q343.1992
95-th percentile195.468
Maximum286.2616
Range286.0784
Interquartile range (IQR)42.5632

Descriptive statistics

Standard deviation74.279193
Coefficient of variation (CV)1.9101314
Kurtosis2.5136484
Mean38.886954
Median Absolute Deviation (MAD)2
Skewness1.9275149
Sum2348694.2
Variance5517.3984
MonotonicityNot monotonic
2024-04-07T17:16:46.803036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.3992 8827
 
14.6%
2.7992 6440
 
10.7%
0.7192 4470
 
7.4%
0.1832 3191
 
5.3%
0.3192 2376
 
3.9%
1.7584 2121
 
3.5%
0.7992 2025
 
3.4%
1.9992 1788
 
3.0%
3.9992 1736
 
2.9%
43.1992 1695
 
2.8%
Other values (32) 25729
42.6%
ValueCountFrequency (%)
0.1832 3191
 
5.3%
0.3192 2376
 
3.9%
0.3992 8827
14.6%
0.636 908
 
1.5%
0.7192 4470
7.4%
0.7992 2025
 
3.4%
1.7192 1044
 
1.7%
1.7584 2121
 
3.5%
1.9592 1430
 
2.4%
1.9992 1788
 
3.0%
ValueCountFrequency (%)
286.2616 1551
2.6%
271.9992 185
 
0.3%
269.9992 211
 
0.3%
195.468 1145
1.9%
190.7256 1255
2.1%
185.5992 1215
2.0%
183.5992 1262
2.1%
174.525 758
1.3%
165.7136 521
 
0.9%
163.9279 554
 
0.9%

Freight
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.152217
Minimum0.0573
Maximum89.4568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:47.042477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0573
5-th percentile0.0573
Q10.1988
median0.7498
Q313.4998
95-th percentile61.0838
Maximum89.4568
Range89.3995
Interquartile range (IQR)13.301

Descriptive statistics

Standard deviation23.212248
Coefficient of variation (CV)1.9101245
Kurtosis2.5136492
Mean12.152217
Median Absolute Deviation (MAD)0.625
Skewness1.927515
Sum733969.61
Variance538.80847
MonotonicityNot monotonic
2024-04-07T17:16:47.250362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.1248 8827
 
14.6%
0.8748 6440
 
10.7%
0.2248 4470
 
7.4%
0.0573 3191
 
5.3%
0.0998 2376
 
3.9%
0.5495 2121
 
3.5%
0.2498 2025
 
3.4%
0.6248 1788
 
3.0%
1.2498 1736
 
2.9%
13.4998 1695
 
2.8%
Other values (32) 25729
42.6%
ValueCountFrequency (%)
0.0573 3191
 
5.3%
0.0998 2376
 
3.9%
0.1248 8827
14.6%
0.1988 908
 
1.5%
0.2248 4470
7.4%
0.2498 2025
 
3.4%
0.5373 1044
 
1.7%
0.5495 2121
 
3.5%
0.6123 1430
 
2.4%
0.6248 1788
 
3.0%
ValueCountFrequency (%)
89.4568 1551
2.6%
84.9998 185
 
0.3%
84.3748 211
 
0.3%
61.0838 1145
1.9%
59.6018 1255
2.1%
57.9998 1215
2.0%
57.3748 1262
2.1%
54.5391 758
1.3%
51.7855 521
 
0.9%
51.2275 554
 
0.9%
Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Minimum2010-12-29 00:00:00
Maximum2014-01-28 00:00:00
2024-04-07T17:16:47.478326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:47.711954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Minimum2011-01-10 00:00:00
Maximum2014-02-09 00:00:00
2024-04-07T17:16:47.959940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:48.186843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Minimum2011-01-05 00:00:00
Maximum2014-02-04 00:00:00
2024-04-07T17:16:48.434369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:48.665346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DimCurrency.CurrencyName
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
US Dollar
33400 
Australian Dollar
12988 
Canadian Dollar
7135 
United Kingdom Pound
6740 
Deutsche Mark
 
76

Length

Max length20
Median length9
Mean length12.664608
Min length9

Characters and Unicode

Total characters764917
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS Dollar
2nd rowUS Dollar
3rd rowUS Dollar
4th rowUS Dollar
5th rowUS Dollar

Common Values

ValueCountFrequency (%)
US Dollar 33400
55.3%
Australian Dollar 12988
 
21.5%
Canadian Dollar 7135
 
11.8%
United Kingdom Pound 6740
 
11.2%
Deutsche Mark 76
 
0.1%
French Franc 59
 
0.1%

Length

2024-04-07T17:16:48.873543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:49.119457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dollar 53523
42.0%
us 33400
26.2%
australian 12988
 
10.2%
canadian 7135
 
5.6%
united 6740
 
5.3%
kingdom 6740
 
5.3%
pound 6740
 
5.3%
deutsche 76
 
0.1%
mark 76
 
0.1%
french 59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 120034
15.7%
a 101039
13.2%
67138
8.8%
o 67003
8.8%
r 66705
8.7%
D 53599
 
7.0%
n 47596
 
6.2%
U 40140
 
5.2%
i 33603
 
4.4%
S 33400
 
4.4%
Other values (16) 134660
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 764917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 120034
15.7%
a 101039
13.2%
67138
8.8%
o 67003
8.8%
r 66705
8.7%
D 53599
 
7.0%
n 47596
 
6.2%
U 40140
 
5.2%
i 33603
 
4.4%
S 33400
 
4.4%
Other values (16) 134660
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 764917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 120034
15.7%
a 101039
13.2%
67138
8.8%
o 67003
8.8%
r 66705
8.7%
D 53599
 
7.0%
n 47596
 
6.2%
U 40140
 
5.2%
i 33603
 
4.4%
S 33400
 
4.4%
Other values (16) 134660
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 764917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 120034
15.7%
a 101039
13.2%
67138
8.8%
o 67003
8.8%
r 66705
8.7%
D 53599
 
7.0%
n 47596
 
6.2%
U 40140
 
5.2%
i 33603
 
4.4%
S 33400
 
4.4%
Other values (16) 134660
17.6%
Distinct34939
Distinct (%)57.8%
Missing1
Missing (%)< 0.1%
Memory size472.0 KiB
2024-04-07T17:16:49.544862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length27
Median length23
Mean length14.078183
Min length7

Characters and Unicode

Total characters850280
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22715 ?
Unique (%)37.6%

Sample

1st rowLucas Hill
2nd rowKelvin Huang
3rd rowCourtney Carter
4th rowAshley Washington
5th rowJeremy Murphy
ValueCountFrequency (%)
l 4215
 
2.7%
a 4168
 
2.7%
m 3785
 
2.4%
j 3166
 
2.0%
c 3067
 
2.0%
e 2303
 
1.5%
r 2186
 
1.4%
d 1964
 
1.3%
s 1585
 
1.0%
k 1114
 
0.7%
Other values (1032) 128153
82.3%
2024-04-07T17:16:50.258588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120803
14.2%
a 82885
 
9.7%
e 69538
 
8.2%
n 60164
 
7.1%
r 53992
 
6.3%
i 43499
 
5.1%
o 39828
 
4.7%
l 36502
 
4.3%
s 27912
 
3.3%
t 21626
 
2.5%
Other values (52) 293531
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 850280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
120803
14.2%
a 82885
 
9.7%
e 69538
 
8.2%
n 60164
 
7.1%
r 53992
 
6.3%
i 43499
 
5.1%
o 39828
 
4.7%
l 36502
 
4.3%
s 27912
 
3.3%
t 21626
 
2.5%
Other values (52) 293531
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 850280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
120803
14.2%
a 82885
 
9.7%
e 69538
 
8.2%
n 60164
 
7.1%
r 53992
 
6.3%
i 43499
 
5.1%
o 39828
 
4.7%
l 36502
 
4.3%
s 27912
 
3.3%
t 21626
 
2.5%
Other values (52) 293531
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 850280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
120803
14.2%
a 82885
 
9.7%
e 69538
 
8.2%
n 60164
 
7.1%
r 53992
 
6.3%
i 43499
 
5.1%
o 39828
 
4.7%
l 36502
 
4.3%
s 27912
 
3.3%
t 21626
 
2.5%
Other values (52) 293531
34.5%
Distinct6139
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Minimum1916-02-10 00:00:00
Maximum1986-06-25 00:00:00
2024-04-07T17:16:50.530360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:50.766062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
M
33273 
S
27125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 33273
55.1%
S 27125
44.9%

Length

2024-04-07T17:16:51.004681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:51.190561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 33273
55.1%
s 27125
44.9%

Most occurring characters

ValueCountFrequency (%)
M 33273
55.1%
S 27125
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 33273
55.1%
S 27125
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 33273
55.1%
S 27125
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 33273
55.1%
S 27125
44.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
M
30381 
F
30017 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 30381
50.3%
F 30017
49.7%

Length

2024-04-07T17:16:51.357625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:51.546106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 30381
50.3%
f 30017
49.7%

Most occurring characters

ValueCountFrequency (%)
M 30381
50.3%
F 30017
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 30381
50.3%
F 30017
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 30381
50.3%
F 30017
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 30381
50.3%
F 30017
49.7%

DimCustomer.YearlyIncome
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59715.057
Minimum10000
Maximum170000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:51.695499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10000
Q130000
median60000
Q380000
95-th percentile130000
Maximum170000
Range160000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation33065.427
Coefficient of variation (CV)0.55372009
Kurtosis0.53210281
Mean59715.057
Median Absolute Deviation (MAD)20000
Skewness0.78395658
Sum3.60667 × 109
Variance1.0933225 × 109
MonotonicityNot monotonic
2024-04-07T17:16:51.863931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60000 9470
15.7%
40000 9181
15.2%
70000 8267
13.7%
30000 6994
11.6%
20000 5128
8.5%
80000 4848
8.0%
10000 3352
 
5.5%
90000 3143
 
5.2%
50000 1977
 
3.3%
100000 1955
 
3.2%
Other values (6) 6083
10.1%
ValueCountFrequency (%)
10000 3352
 
5.5%
20000 5128
8.5%
30000 6994
11.6%
40000 9181
15.2%
50000 1977
 
3.3%
60000 9470
15.7%
70000 8267
13.7%
80000 4848
8.0%
90000 3143
 
5.2%
100000 1955
 
3.2%
ValueCountFrequency (%)
170000 476
 
0.8%
160000 359
 
0.6%
150000 385
 
0.6%
130000 1906
 
3.2%
120000 1242
 
2.1%
110000 1715
 
2.8%
100000 1955
 
3.2%
90000 3143
 
5.2%
80000 4848
8.0%
70000 8267
13.7%

DimCustomer.TotalChildren
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8507401
Minimum0
Maximum5
Zeros17048
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:52.054799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6210704
Coefficient of variation (CV)0.87590387
Kurtosis-0.97268895
Mean1.8507401
Median Absolute Deviation (MAD)1
Skewness0.46305669
Sum111781
Variance2.6278693
MonotonicityNot monotonic
2024-04-07T17:16:52.231674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17048
28.2%
2 12285
20.3%
1 11561
19.1%
4 7748
12.8%
3 7061
11.7%
5 4695
 
7.8%
ValueCountFrequency (%)
0 17048
28.2%
1 11561
19.1%
2 12285
20.3%
3 7061
11.7%
4 7748
12.8%
5 4695
 
7.8%
ValueCountFrequency (%)
5 4695
 
7.8%
4 7748
12.8%
3 7061
11.7%
2 12285
20.3%
1 11561
19.1%
0 17048
28.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Bachelors
18144 
Partial College
16623 
Graduate Degree
10603 
High School
10320 
Partial High School
4708 

Length

Max length19
Median length15
Mean length12.825888
Min length9

Characters and Unicode

Total characters774658
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate Degree
2nd rowHigh School
3rd rowBachelors
4th rowBachelors
5th rowBachelors

Common Values

ValueCountFrequency (%)
Bachelors 18144
30.0%
Partial College 16623
27.5%
Graduate Degree 10603
17.6%
High School 10320
17.1%
Partial High School 4708
 
7.8%

Length

2024-04-07T17:16:52.416028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:52.643578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
partial 21331
19.9%
bachelors 18144
16.9%
college 16623
15.5%
high 15028
14.0%
school 15028
14.0%
graduate 10603
9.9%
degree 10603
9.9%

Most occurring characters

ValueCountFrequency (%)
e 93802
12.1%
l 87749
11.3%
a 82012
10.6%
o 64823
 
8.4%
r 60681
 
7.8%
h 48200
 
6.2%
46962
 
6.1%
g 42254
 
5.5%
i 36359
 
4.7%
c 33172
 
4.3%
Other values (11) 178644
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 774658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 93802
12.1%
l 87749
11.3%
a 82012
10.6%
o 64823
 
8.4%
r 60681
 
7.8%
h 48200
 
6.2%
46962
 
6.1%
g 42254
 
5.5%
i 36359
 
4.7%
c 33172
 
4.3%
Other values (11) 178644
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 774658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 93802
12.1%
l 87749
11.3%
a 82012
10.6%
o 64823
 
8.4%
r 60681
 
7.8%
h 48200
 
6.2%
46962
 
6.1%
g 42254
 
5.5%
i 36359
 
4.7%
c 33172
 
4.3%
Other values (11) 178644
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 774658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 93802
12.1%
l 87749
11.3%
a 82012
10.6%
o 64823
 
8.4%
r 60681
 
7.8%
h 48200
 
6.2%
46962
 
6.1%
g 42254
 
5.5%
i 36359
 
4.7%
c 33172
 
4.3%
Other values (11) 178644
23.1%

DimCustomer.EnglishOccupation
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Professional
18995 
Skilled Manual
14261 
Management
10594 
Clerical
9624 
Manual
6924 

Length

Max length14
Median length12
Mean length10.796218
Min length6

Characters and Unicode

Total characters652070
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManagement
2nd rowProfessional
3rd rowSkilled Manual
4th rowProfessional
5th rowSkilled Manual

Common Values

ValueCountFrequency (%)
Professional 18995
31.4%
Skilled Manual 14261
23.6%
Management 10594
17.5%
Clerical 9624
15.9%
Manual 6924
 
11.5%

Length

2024-04-07T17:16:52.859625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:53.119514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manual 21185
28.4%
professional 18995
25.4%
skilled 14261
19.1%
management 10594
14.2%
clerical 9624
12.9%

Most occurring characters

ValueCountFrequency (%)
a 92177
14.1%
l 87950
13.5%
e 64068
9.8%
n 61368
9.4%
i 42880
 
6.6%
o 37990
 
5.8%
s 37990
 
5.8%
M 31779
 
4.9%
r 28619
 
4.4%
u 21185
 
3.2%
Other values (11) 146064
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 652070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 92177
14.1%
l 87950
13.5%
e 64068
9.8%
n 61368
9.4%
i 42880
 
6.6%
o 37990
 
5.8%
s 37990
 
5.8%
M 31779
 
4.9%
r 28619
 
4.4%
u 21185
 
3.2%
Other values (11) 146064
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 652070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 92177
14.1%
l 87950
13.5%
e 64068
9.8%
n 61368
9.4%
i 42880
 
6.6%
o 37990
 
5.8%
s 37990
 
5.8%
M 31779
 
4.9%
r 28619
 
4.4%
u 21185
 
3.2%
Other values (11) 146064
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 652070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 92177
14.1%
l 87950
13.5%
e 64068
9.8%
n 61368
9.4%
i 42880
 
6.6%
o 37990
 
5.8%
s 37990
 
5.8%
M 31779
 
4.9%
r 28619
 
4.4%
u 21185
 
3.2%
Other values (11) 146064
22.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
1
41699 
0
18699 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%

Length

2024-04-07T17:16:53.465910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:53.731285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%

Most occurring characters

ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 41699
69.0%
0 18699
31.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
2
20522 
1
15812 
0
14068 
3
5688 
4
4308 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters60398
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%

Length

2024-04-07T17:16:54.067293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:54.464957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%

Most occurring characters

ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 60398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 20522
34.0%
1 15812
26.2%
0 14068
23.3%
3 5688
 
9.4%
4 4308
 
7.1%
Distinct1124
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Minimum2010-12-29 00:00:00
Maximum2014-01-28 00:00:00
2024-04-07T17:16:54.832093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:55.180147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
0-1 Miles
21307 
5-10 Miles
10615 
1-2 Miles
10170 
2-5 Miles
10084 
10+ Miles
8222 

Length

Max length10
Median length9
Mean length9.1757509
Min length9

Characters and Unicode

Total characters554197
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-2 Miles
2nd row5-10 Miles
3rd row2-5 Miles
4th row2-5 Miles
5th row2-5 Miles

Common Values

ValueCountFrequency (%)
0-1 Miles 21307
35.3%
5-10 Miles 10615
17.6%
1-2 Miles 10170
16.8%
2-5 Miles 10084
16.7%
10+ Miles 8222
 
13.6%

Length

2024-04-07T17:16:55.563123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:55.818605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
miles 60398
50.0%
0-1 21307
 
17.6%
5-10 10615
 
8.8%
1-2 10170
 
8.4%
2-5 10084
 
8.3%
10 8222
 
6.8%

Most occurring characters

ValueCountFrequency (%)
60398
10.9%
M 60398
10.9%
i 60398
10.9%
l 60398
10.9%
e 60398
10.9%
s 60398
10.9%
- 52176
9.4%
1 50314
9.1%
0 40144
7.2%
5 20699
 
3.7%
Other values (2) 28476
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 554197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
60398
10.9%
M 60398
10.9%
i 60398
10.9%
l 60398
10.9%
e 60398
10.9%
s 60398
10.9%
- 52176
9.4%
1 50314
9.1%
0 40144
7.2%
5 20699
 
3.7%
Other values (2) 28476
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 554197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
60398
10.9%
M 60398
10.9%
i 60398
10.9%
l 60398
10.9%
e 60398
10.9%
s 60398
10.9%
- 52176
9.4%
1 50314
9.1%
0 40144
7.2%
5 20699
 
3.7%
Other values (2) 28476
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 554197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
60398
10.9%
M 60398
10.9%
i 60398
10.9%
l 60398
10.9%
e 60398
10.9%
s 60398
10.9%
- 52176
9.4%
1 50314
9.1%
0 40144
7.2%
5 20699
 
3.7%
Other values (2) 28476
5.1%

DimProduct.ProductSubcategoryKey
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.351469
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:56.012410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median28
Q337
95-th percentile37
Maximum37
Range36
Interquartile range (IQR)34

Descriptive statistics

Standard deviation13.556081
Coefficient of variation (CV)0.58052369
Kurtosis-1.074861
Mean23.351469
Median Absolute Deviation (MAD)9
Skewness-0.68821771
Sum1410382
Variance183.76732
MonotonicityNot monotonic
2024-04-07T17:16:56.210919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
37 17332
28.7%
2 8068
13.4%
28 7981
13.2%
31 6440
 
10.7%
1 4970
 
8.2%
21 3332
 
5.5%
19 2190
 
3.6%
3 2167
 
3.6%
30 2121
 
3.5%
20 1430
 
2.4%
Other values (7) 4367
 
7.2%
ValueCountFrequency (%)
1 4970
8.2%
2 8068
13.4%
3 2167
 
3.6%
19 2190
 
3.6%
20 1430
 
2.4%
21 3332
5.5%
22 1019
 
1.7%
23 568
 
0.9%
25 562
 
0.9%
26 328
 
0.5%
ValueCountFrequency (%)
37 17332
28.7%
32 733
 
1.2%
31 6440
 
10.7%
30 2121
 
3.5%
29 908
 
1.5%
28 7981
13.2%
27 249
 
0.4%
26 328
 
0.5%
25 562
 
0.9%
23 568
 
0.9%
Distinct130
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
2024-04-07T17:16:56.549959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length31
Median length26
Mean length19.308835
Min length12

Characters and Unicode

Total characters1166215
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoad-650 Black, 62
2nd rowRoad-150 Red, 48
3rd rowRoad-650 Black, 58
4th rowRoad-150 Red, 56
5th rowRoad-650 Red, 52
ValueCountFrequency (%)
tire 14141
 
7.1%
mountain 11679
 
5.9%
8334
 
4.2%
bottle 7981
 
4.0%
black 7394
 
3.7%
tube 6959
 
3.5%
road 6916
 
3.5%
sport-100 6440
 
3.3%
helmet 6440
 
3.3%
oz 4977
 
2.5%
Other values (71) 116660
58.9%
2024-04-07T17:16:57.162421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
137523
 
11.8%
e 97490
 
8.4%
o 75934
 
6.5%
t 65916
 
5.7%
a 62687
 
5.4%
i 48872
 
4.2%
n 45744
 
3.9%
0 44110
 
3.8%
l 44076
 
3.8%
r 43494
 
3.7%
Other values (47) 500369
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1166215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
137523
 
11.8%
e 97490
 
8.4%
o 75934
 
6.5%
t 65916
 
5.7%
a 62687
 
5.4%
i 48872
 
4.2%
n 45744
 
3.9%
0 44110
 
3.8%
l 44076
 
3.8%
r 43494
 
3.7%
Other values (47) 500369
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1166215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
137523
 
11.8%
e 97490
 
8.4%
o 75934
 
6.5%
t 65916
 
5.7%
a 62687
 
5.4%
i 48872
 
4.2%
n 45744
 
3.9%
0 44110
 
3.8%
l 44076
 
3.8%
r 43494
 
3.7%
Other values (47) 500369
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1166215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
137523
 
11.8%
e 97490
 
8.4%
o 75934
 
6.5%
t 65916
 
5.7%
a 62687
 
5.4%
i 48872
 
4.2%
n 45744
 
3.9%
0 44110
 
3.8%
l 44076
 
3.8%
r 43494
 
3.7%
Other values (47) 500369
42.9%
Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Tires and Tubes
17332 
Road Bikes
8068 
Bottles and Cages
7981 
Helmets
6440 
Mountain Bikes
4970 
Other values (12)
15607 

Length

Max length17
Median length14
Mean length11.767294
Min length4

Characters and Unicode

Total characters710721
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoad Bikes
2nd rowRoad Bikes
3rd rowRoad Bikes
4th rowRoad Bikes
5th rowRoad Bikes

Common Values

ValueCountFrequency (%)
Tires and Tubes 17332
28.7%
Road Bikes 8068
13.4%
Bottles and Cages 7981
13.2%
Helmets 6440
 
10.7%
Mountain Bikes 4970
 
8.2%
Jerseys 3332
 
5.5%
Caps 2190
 
3.6%
Touring Bikes 2167
 
3.6%
Fenders 2121
 
3.5%
Gloves 1430
 
2.4%
Other values (7) 4367
 
7.2%

Length

2024-04-07T17:16:57.419813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 25313
19.8%
tires 17332
13.6%
tubes 17332
13.6%
bikes 15205
11.9%
road 8068
 
6.3%
bottles 7981
 
6.3%
cages 7981
 
6.3%
helmets 6440
 
5.0%
mountain 4970
 
3.9%
jerseys 3332
 
2.6%
Other values (13) 13585
10.7%

Most occurring characters

ValueCountFrequency (%)
e 94002
13.2%
s 89605
12.6%
67141
 
9.4%
a 51473
 
7.2%
n 41431
 
5.8%
i 40984
 
5.8%
T 36831
 
5.2%
d 36484
 
5.1%
t 29935
 
4.2%
r 27612
 
3.9%
Other values (23) 195223
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 710721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 94002
13.2%
s 89605
12.6%
67141
 
9.4%
a 51473
 
7.2%
n 41431
 
5.8%
i 40984
 
5.8%
T 36831
 
5.2%
d 36484
 
5.1%
t 29935
 
4.2%
r 27612
 
3.9%
Other values (23) 195223
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 710721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 94002
13.2%
s 89605
12.6%
67141
 
9.4%
a 51473
 
7.2%
n 41431
 
5.8%
i 40984
 
5.8%
T 36831
 
5.2%
d 36484
 
5.1%
t 29935
 
4.2%
r 27612
 
3.9%
Other values (23) 195223
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 710721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 94002
13.2%
s 89605
12.6%
67141
 
9.4%
a 51473
 
7.2%
n 41431
 
5.8%
i 40984
 
5.8%
T 36831
 
5.2%
d 36484
 
5.1%
t 29935
 
4.2%
r 27612
 
3.9%
Other values (23) 195223
27.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Accessories
36092 
Bikes
15205 
Clothing
9101 

Length

Max length11
Median length11
Mean length9.0374681
Min length5

Characters and Unicode

Total characters545845
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBikes
2nd rowBikes
3rd rowBikes
4th rowBikes
5th rowBikes

Common Values

ValueCountFrequency (%)
Accessories 36092
59.8%
Bikes 15205
25.2%
Clothing 9101
 
15.1%

Length

2024-04-07T17:16:57.627428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:57.840666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
accessories 36092
59.8%
bikes 15205
25.2%
clothing 9101
 
15.1%

Most occurring characters

ValueCountFrequency (%)
s 123481
22.6%
e 87389
16.0%
c 72184
13.2%
i 60398
11.1%
o 45193
 
8.3%
A 36092
 
6.6%
r 36092
 
6.6%
B 15205
 
2.8%
k 15205
 
2.8%
C 9101
 
1.7%
Other values (5) 45505
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 545845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 123481
22.6%
e 87389
16.0%
c 72184
13.2%
i 60398
11.1%
o 45193
 
8.3%
A 36092
 
6.6%
r 36092
 
6.6%
B 15205
 
2.8%
k 15205
 
2.8%
C 9101
 
1.7%
Other values (5) 45505
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 545845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 123481
22.6%
e 87389
16.0%
c 72184
13.2%
i 60398
11.1%
o 45193
 
8.3%
A 36092
 
6.6%
r 36092
 
6.6%
B 15205
 
2.8%
k 15205
 
2.8%
C 9101
 
1.7%
Other values (5) 45505
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 545845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 123481
22.6%
e 87389
16.0%
c 72184
13.2%
i 60398
11.1%
o 45193
 
8.3%
A 36092
 
6.6%
r 36092
 
6.6%
B 15205
 
2.8%
k 15205
 
2.8%
C 9101
 
1.7%
Other values (5) 45505
 
8.3%

DimPromotion.EnglishPromotionName
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
No Discount
58247 
Volume Discount 11 to 14
 
2118
Touring-3000 Promotion
 
20
Touring-1000 Promotion
 
13

Length

Max length24
Median length11
Mean length11.461886
Min length11

Characters and Unicode

Total characters692275
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Discount
2nd rowNo Discount
3rd rowNo Discount
4th rowNo Discount
5th rowNo Discount

Common Values

ValueCountFrequency (%)
No Discount 58247
96.4%
Volume Discount 11 to 14 2118
 
3.5%
Touring-3000 Promotion 20
 
< 0.1%
Touring-1000 Promotion 13
 
< 0.1%

Length

2024-04-07T17:16:58.023052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:58.231094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
discount 60365
47.5%
no 58247
45.8%
volume 2118
 
1.7%
11 2118
 
1.7%
to 2118
 
1.7%
14 2118
 
1.7%
promotion 33
 
< 0.1%
touring-3000 20
 
< 0.1%
touring-1000 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 122980
17.8%
66752
9.6%
u 62516
9.0%
t 62516
9.0%
i 60431
8.7%
n 60431
8.7%
D 60365
8.7%
s 60365
8.7%
c 60365
8.7%
N 58247
8.4%
Other values (13) 17307
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 692275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 122980
17.8%
66752
9.6%
u 62516
9.0%
t 62516
9.0%
i 60431
8.7%
n 60431
8.7%
D 60365
8.7%
s 60365
8.7%
c 60365
8.7%
N 58247
8.4%
Other values (13) 17307
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 692275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 122980
17.8%
66752
9.6%
u 62516
9.0%
t 62516
9.0%
i 60431
8.7%
n 60431
8.7%
D 60365
8.7%
s 60365
8.7%
c 60365
8.7%
N 58247
8.4%
Other values (13) 17307
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 692275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 122980
17.8%
66752
9.6%
u 62516
9.0%
t 62516
9.0%
i 60431
8.7%
n 60431
8.7%
D 60365
8.7%
s 60365
8.7%
c 60365
8.7%
N 58247
8.4%
Other values (13) 17307
 
2.5%

DimPromotion.EnglishPromotionType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
No Discount
58247 
Volume Discount
 
2118
New Product
 
33

Length

Max length15
Median length11
Mean length11.14027
Min length11

Characters and Unicode

Total characters672850
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Discount
2nd rowNo Discount
3rd rowNo Discount
4th rowNo Discount
5th rowNo Discount

Common Values

ValueCountFrequency (%)
No Discount 58247
96.4%
Volume Discount 2118
 
3.5%
New Product 33
 
0.1%

Length

2024-04-07T17:16:58.452307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:58.695731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
discount 60365
50.0%
no 58247
48.2%
volume 2118
 
1.8%
new 33
 
< 0.1%
product 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 120763
17.9%
u 62516
9.3%
t 60398
9.0%
60398
9.0%
c 60398
9.0%
D 60365
9.0%
i 60365
9.0%
s 60365
9.0%
n 60365
9.0%
N 58280
8.7%
Other values (8) 8637
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 672850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 120763
17.9%
u 62516
9.3%
t 60398
9.0%
60398
9.0%
c 60398
9.0%
D 60365
9.0%
i 60365
9.0%
s 60365
9.0%
n 60365
9.0%
N 58280
8.7%
Other values (8) 8637
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 672850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 120763
17.9%
u 62516
9.3%
t 60398
9.0%
60398
9.0%
c 60398
9.0%
D 60365
9.0%
i 60365
9.0%
s 60365
9.0%
n 60365
9.0%
N 58280
8.7%
Other values (8) 8637
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 672850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 120763
17.9%
u 62516
9.3%
t 60398
9.0%
60398
9.0%
c 60398
9.0%
D 60365
9.0%
i 60365
9.0%
s 60365
9.0%
n 60365
9.0%
N 58280
8.7%
Other values (8) 8637
 
1.3%

DimPromotion.EnglishPromotionCategory
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
No Discount
58247 
Reseller
 
2151

Length

Max length11
Median length11
Mean length10.893159
Min length8

Characters and Unicode

Total characters657925
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Discount
2nd rowNo Discount
3rd rowNo Discount
4th rowNo Discount
5th rowNo Discount

Common Values

ValueCountFrequency (%)
No Discount 58247
96.4%
Reseller 2151
 
3.6%

Length

2024-04-07T17:16:58.882121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:59.085691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 58247
49.1%
discount 58247
49.1%
reseller 2151
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o 116494
17.7%
s 60398
9.2%
N 58247
8.9%
58247
8.9%
D 58247
8.9%
i 58247
8.9%
c 58247
8.9%
u 58247
8.9%
n 58247
8.9%
t 58247
8.9%
Other values (4) 15057
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 657925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 116494
17.7%
s 60398
9.2%
N 58247
8.9%
58247
8.9%
D 58247
8.9%
i 58247
8.9%
c 58247
8.9%
u 58247
8.9%
n 58247
8.9%
t 58247
8.9%
Other values (4) 15057
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 657925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 116494
17.7%
s 60398
9.2%
N 58247
8.9%
58247
8.9%
D 58247
8.9%
i 58247
8.9%
c 58247
8.9%
u 58247
8.9%
n 58247
8.9%
t 58247
8.9%
Other values (4) 15057
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 657925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 116494
17.7%
s 60398
9.2%
N 58247
8.9%
58247
8.9%
D 58247
8.9%
i 58247
8.9%
c 58247
8.9%
u 58247
8.9%
n 58247
8.9%
t 58247
8.9%
Other values (4) 15057
 
2.3%

DimSalesTerritory.SalesTerritoryRegion
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Australia
13345 
Southwest
12265 
Northwest
8993 
Canada
7620 
United Kingdom
6906 
Other values (5)
11269 

Length

Max length14
Median length9
Mean length8.7302229
Min length6

Characters and Unicode

Total characters527288
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthwest
2nd rowSouthwest
3rd rowSouthwest
4th rowSouthwest
5th rowSouthwest

Common Values

ValueCountFrequency (%)
Australia 13345
22.1%
Southwest 12265
20.3%
Northwest 8993
14.9%
Canada 7620
12.6%
United Kingdom 6906
11.4%
Germany 5625
9.3%
France 5558
9.2%
Southeast 39
 
0.1%
Northeast 27
 
< 0.1%
Central 20
 
< 0.1%

Length

2024-04-07T17:16:59.279736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:16:59.552360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
australia 13345
19.8%
southwest 12265
18.2%
northwest 8993
13.4%
canada 7620
11.3%
united 6906
10.3%
kingdom 6906
10.3%
germany 5625
8.4%
france 5558
8.3%
southeast 39
 
0.1%
northeast 27
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 62919
 
11.9%
a 60819
 
11.5%
e 39433
 
7.5%
s 34669
 
6.6%
r 33568
 
6.4%
n 32635
 
6.2%
o 28230
 
5.4%
i 27157
 
5.2%
u 25649
 
4.9%
d 21432
 
4.1%
Other values (16) 160777
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 527288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 62919
 
11.9%
a 60819
 
11.5%
e 39433
 
7.5%
s 34669
 
6.6%
r 33568
 
6.4%
n 32635
 
6.2%
o 28230
 
5.4%
i 27157
 
5.2%
u 25649
 
4.9%
d 21432
 
4.1%
Other values (16) 160777
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 527288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 62919
 
11.9%
a 60819
 
11.5%
e 39433
 
7.5%
s 34669
 
6.6%
r 33568
 
6.4%
n 32635
 
6.2%
o 28230
 
5.4%
i 27157
 
5.2%
u 25649
 
4.9%
d 21432
 
4.1%
Other values (16) 160777
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 527288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 62919
 
11.9%
a 60819
 
11.5%
e 39433
 
7.5%
s 34669
 
6.6%
r 33568
 
6.4%
n 32635
 
6.2%
o 28230
 
5.4%
i 27157
 
5.2%
u 25649
 
4.9%
d 21432
 
4.1%
Other values (16) 160777
30.5%

DimSalesTerritory.SalesTerritoryCountry
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
United States
21344 
Australia
13345 
Canada
7620 
United Kingdom
6906 
Germany
5625 

Length

Max length14
Median length13
Mean length10.144442
Min length6

Characters and Unicode

Total characters612704
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 21344
35.3%
Australia 13345
22.1%
Canada 7620
 
12.6%
United Kingdom 6906
 
11.4%
Germany 5625
 
9.3%
France 5558
 
9.2%

Length

2024-04-07T17:16:59.817551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:17:00.056992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
united 28250
31.9%
states 21344
24.1%
australia 13345
15.1%
canada 7620
 
8.6%
kingdom 6906
 
7.8%
germany 5625
 
6.3%
france 5558
 
6.3%

Most occurring characters

ValueCountFrequency (%)
t 84283
13.8%
a 82077
13.4%
e 60777
9.9%
n 53959
8.8%
i 48501
 
7.9%
d 42776
 
7.0%
s 34689
 
5.7%
U 28250
 
4.6%
28250
 
4.6%
r 24528
 
4.0%
Other values (13) 124614
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 612704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 84283
13.8%
a 82077
13.4%
e 60777
9.9%
n 53959
8.8%
i 48501
 
7.9%
d 42776
 
7.0%
s 34689
 
5.7%
U 28250
 
4.6%
28250
 
4.6%
r 24528
 
4.0%
Other values (13) 124614
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 612704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 84283
13.8%
a 82077
13.4%
e 60777
9.9%
n 53959
8.8%
i 48501
 
7.9%
d 42776
 
7.0%
s 34689
 
5.7%
U 28250
 
4.6%
28250
 
4.6%
r 24528
 
4.0%
Other values (13) 124614
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 612704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 84283
13.8%
a 82077
13.4%
e 60777
9.9%
n 53959
8.8%
i 48501
 
7.9%
d 42776
 
7.0%
s 34689
 
5.7%
U 28250
 
4.6%
28250
 
4.6%
r 24528
 
4.0%
Other values (13) 124614
20.3%

DimSalesTerritory.SalesTerritoryGroup
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
North America
28964 
Europe
18089 
Pacific
13345 

Length

Max length13
Median length7
Mean length9.5778171
Min length6

Characters and Unicode

Total characters578481
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowNorth America
3rd rowNorth America
4th rowNorth America
5th rowNorth America

Common Values

ValueCountFrequency (%)
North America 28964
48.0%
Europe 18089
29.9%
Pacific 13345
22.1%

Length

2024-04-07T17:17:00.286493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:17:00.513846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
north 28964
32.4%
america 28964
32.4%
europe 18089
20.2%
pacific 13345
14.9%

Most occurring characters

ValueCountFrequency (%)
r 76017
13.1%
c 55654
9.6%
i 55654
9.6%
e 47053
 
8.1%
o 47053
 
8.1%
a 42309
 
7.3%
m 28964
 
5.0%
N 28964
 
5.0%
A 28964
 
5.0%
28964
 
5.0%
Other values (7) 138885
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 578481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 76017
13.1%
c 55654
9.6%
i 55654
9.6%
e 47053
 
8.1%
o 47053
 
8.1%
a 42309
 
7.3%
m 28964
 
5.0%
N 28964
 
5.0%
A 28964
 
5.0%
28964
 
5.0%
Other values (7) 138885
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 578481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 76017
13.1%
c 55654
9.6%
i 55654
9.6%
e 47053
 
8.1%
o 47053
 
8.1%
a 42309
 
7.3%
m 28964
 
5.0%
N 28964
 
5.0%
A 28964
 
5.0%
28964
 
5.0%
Other values (7) 138885
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 578481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 76017
13.1%
c 55654
9.6%
i 55654
9.6%
e 47053
 
8.1%
o 47053
 
8.1%
a 42309
 
7.3%
m 28964
 
5.0%
N 28964
 
5.0%
A 28964
 
5.0%
28964
 
5.0%
Other values (7) 138885
24.0%

Cost
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337.10483
Minimum1.097
Maximum2547.0126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:17:00.712512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.097
5-th percentile1.097
Q13.8081
median14.3653
Q3351.2787
95-th percentile1775.3382
Maximum2547.0126
Range2545.9156
Interquartile range (IQR)347.4706

Descriptive statistics

Standard deviation649.77051
Coefficient of variation (CV)1.9275028
Kurtosis2.6559275
Mean337.10483
Median Absolute Deviation (MAD)11.975
Skewness1.9456942
Sum20360457
Variance422201.72
MonotonicityNot monotonic
2024-04-07T17:17:01.574228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2.3903 8827
 
14.6%
16.7603 6440
 
10.7%
1.097 3191
 
5.3%
1.9113 2376
 
3.9%
4.3063 2280
 
3.8%
7.8663 2190
 
3.6%
10.5284 2121
 
3.5%
4.7853 2025
 
3.4%
11.9703 1788
 
3.0%
43.7413 1736
 
2.9%
Other values (35) 27424
45.4%
ValueCountFrequency (%)
1.097 3191
 
5.3%
1.9113 2376
 
3.9%
2.3903 8827
14.6%
3.8081 908
 
1.5%
4.3063 2280
 
3.8%
4.7853 2025
 
3.4%
7.8663 2190
 
3.6%
10.2938 1044
 
1.7%
10.5284 2121
 
3.5%
11.7308 1430
 
2.4%
ValueCountFrequency (%)
2547.0126 1551
2.6%
2269.1534 185
 
0.3%
2252.4684 211
 
0.3%
1811.4997 706
1.2%
1775.3382 439
 
0.7%
1732.2653 1255
2.1%
1549.7479 758
1.3%
1509.2185 1215
2.0%
1492.9553 1262
2.1%
1335.355 521
 
0.9%

Profit
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.98208
Minimum1.1237
Maximum1130.8366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:17:01.801804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1237
5-th percentile1.193
Q12.5997
median13.0197
Q3139.6414
95-th percentile810.7715
Maximum1130.8366
Range1129.7129
Interquartile range (IQR)137.0417

Descriptive statistics

Standard deviation282.06958
Coefficient of variation (CV)1.8933121
Kurtosis2.5053805
Mean148.98208
Median Absolute Deviation (MAD)10.42
Skewness1.9515348
Sum8998219.8
Variance79563.247
MonotonicityNot monotonic
2024-04-07T17:17:02.027309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2.5997 8827
 
14.6%
18.2297 6440
 
10.7%
1.193 3191
 
5.3%
2.0787 2376
 
3.9%
4.6837 2280
 
3.8%
1.1237 2190
 
3.6%
11.4516 2121
 
3.5%
5.2047 2025
 
3.4%
13.0197 1788
 
3.0%
6.2487 1736
 
2.9%
Other values (35) 27424
45.4%
ValueCountFrequency (%)
1.1237 2190
 
3.6%
1.193 3191
 
5.3%
2.0787 2376
 
3.9%
2.5997 8827
14.6%
4.1419 908
 
1.5%
4.6837 2280
 
3.8%
5.2047 2025
 
3.4%
6.2487 1736
 
2.9%
6.7487 1596
 
2.6%
11.1962 1044
 
1.7%
ValueCountFrequency (%)
1130.8366 185
 
0.3%
1122.5216 211
 
0.3%
1031.2574 1551
2.6%
810.7715 1215
2.0%
802.0347 1262
2.1%
736.0646 521
 
0.9%
728.1328 554
 
0.9%
668.0118 439
 
0.7%
651.8047 1255
2.1%
631.8503 706
1.2%

YearlyIncomeRange
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
$50,000 - $79,999
19714 
$30,000 - $49,999
16175 
$80,000 - $170,000
16029 
$10,000 - $29,999
8480 

Length

Max length18
Median length17
Mean length17.26539
Min length17

Characters and Unicode

Total characters1042795
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$80,000 - $170,000
2nd row$50,000 - $79,999
3rd row$50,000 - $79,999
4th row$50,000 - $79,999
5th row$50,000 - $79,999

Common Values

ValueCountFrequency (%)
$50,000 - $79,999 19714
32.6%
$30,000 - $49,999 16175
26.8%
$80,000 - $170,000 16029
26.5%
$10,000 - $29,999 8480
14.0%

Length

2024-04-07T17:17:02.240029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:17:02.541840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
60398
33.3%
50,000 19714
 
10.9%
79,999 19714
 
10.9%
30,000 16175
 
8.9%
49,999 16175
 
8.9%
80,000 16029
 
8.8%
170,000 16029
 
8.8%
10,000 8480
 
4.7%
29,999 8480
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 305708
29.3%
9 177476
17.0%
$ 120796
 
11.6%
, 120796
 
11.6%
120796
 
11.6%
- 60398
 
5.8%
7 35743
 
3.4%
1 24509
 
2.4%
5 19714
 
1.9%
3 16175
 
1.6%
Other values (3) 40684
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1042795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 305708
29.3%
9 177476
17.0%
$ 120796
 
11.6%
, 120796
 
11.6%
120796
 
11.6%
- 60398
 
5.8%
7 35743
 
3.4%
1 24509
 
2.4%
5 19714
 
1.9%
3 16175
 
1.6%
Other values (3) 40684
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1042795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 305708
29.3%
9 177476
17.0%
$ 120796
 
11.6%
, 120796
 
11.6%
120796
 
11.6%
- 60398
 
5.8%
7 35743
 
3.4%
1 24509
 
2.4%
5 19714
 
1.9%
3 16175
 
1.6%
Other values (3) 40684
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1042795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 305708
29.3%
9 177476
17.0%
$ 120796
 
11.6%
, 120796
 
11.6%
120796
 
11.6%
- 60398
 
5.8%
7 35743
 
3.4%
1 24509
 
2.4%
5 19714
 
1.9%
3 16175
 
1.6%
Other values (3) 40684
 
3.9%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.38435
Minimum37
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size472.0 KiB
2024-04-07T17:17:02.789451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile39
Q145
median52
Q362
95-th percentile76
Maximum108
Range71
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.334976
Coefficient of variation (CV)0.20842348
Kurtosis-0.027375664
Mean54.38435
Median Absolute Deviation (MAD)8
Skewness0.70210222
Sum3284706
Variance128.48167
MonotonicityNot monotonic
2024-04-07T17:17:03.023392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 2319
 
3.8%
42 2271
 
3.8%
48 2268
 
3.8%
44 2230
 
3.7%
50 2216
 
3.7%
52 2213
 
3.7%
45 2181
 
3.6%
51 2151
 
3.6%
43 2139
 
3.5%
47 2091
 
3.5%
Other values (61) 38319
63.4%
ValueCountFrequency (%)
37 241
 
0.4%
38 1230
2.0%
39 1636
2.7%
40 1909
3.2%
41 1762
2.9%
42 2271
3.8%
43 2139
3.5%
44 2230
3.7%
45 2181
3.6%
46 1968
3.3%
ValueCountFrequency (%)
108 3
 
< 0.1%
107 2
 
< 0.1%
106 10
< 0.1%
105 9
< 0.1%
103 2
 
< 0.1%
102 2
 
< 0.1%
101 4
 
< 0.1%
100 8
< 0.1%
99 11
< 0.1%
98 8
< 0.1%

Age Range
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size472.0 KiB
Over 45
44799 
31-45
15599 

Length

Max length7
Median length7
Mean length6.4834597
Min length5

Characters and Unicode

Total characters391588
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOver 45
2nd row31-45
3rd rowOver 45
4th rowOver 45
5th rowOver 45

Common Values

ValueCountFrequency (%)
Over 45 44799
74.2%
31-45 15599
 
25.8%

Length

2024-04-07T17:17:03.263451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T17:17:03.505203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
over 44799
42.6%
45 44799
42.6%
31-45 15599
 
14.8%

Most occurring characters

ValueCountFrequency (%)
4 60398
15.4%
5 60398
15.4%
O 44799
11.4%
v 44799
11.4%
e 44799
11.4%
r 44799
11.4%
44799
11.4%
3 15599
 
4.0%
1 15599
 
4.0%
- 15599
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 391588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 60398
15.4%
5 60398
15.4%
O 44799
11.4%
v 44799
11.4%
e 44799
11.4%
r 44799
11.4%
44799
11.4%
3 15599
 
4.0%
1 15599
 
4.0%
- 15599
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 391588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 60398
15.4%
5 60398
15.4%
O 44799
11.4%
v 44799
11.4%
e 44799
11.4%
r 44799
11.4%
44799
11.4%
3 15599
 
4.0%
1 15599
 
4.0%
- 15599
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 391588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 60398
15.4%
5 60398
15.4%
O 44799
11.4%
v 44799
11.4%
e 44799
11.4%
r 44799
11.4%
44799
11.4%
3 15599
 
4.0%
1 15599
 
4.0%
- 15599
 
4.0%

Interactions

2024-04-07T17:16:32.654588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:23.408852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:27.644084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:32.022668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:35.596710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:39.410707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:43.755914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:47.643650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:51.375612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:56.082674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:59.727317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:03.564638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:08.368735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:11.920753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:15.810872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:20.268770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:24.504088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:28.099294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:32.877635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:23.641002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:27.969711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:32.230050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:35.805320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:39.711239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:43.979751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:47.865441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:51.664215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:56.317471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:59.950440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:03.801874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:08.586091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:12.158065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:16.031766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-04-07T17:16:35.409113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:26.317704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:30.963984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:34.603108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:38.294613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:42.731257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:46.622584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:50.346275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:55.037724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:58.728176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:02.506358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:07.332126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:10.929196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:14.745696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:19.242163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:23.434172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:27.116228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:31.614550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:35.631928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:26.521579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:31.169821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:34.792999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:38.485671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:42.938583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:46.826600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:50.554422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:55.246124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:58.913023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:02.703996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:07.540012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:11.112933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:14.969542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:19.427979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:23.634994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:27.315866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:31.819387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:35.858210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:26.735002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:31.409294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:34.998487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:38.719345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:43.155428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:47.050011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:50.766868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:55.481329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:59.116981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:02.933732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:07.761486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:11.317356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:15.185913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:19.642914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:23.853607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:27.524799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:32.033614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:36.060435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:27.016237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:31.609271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:35.180300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:38.907026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:43.351391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:47.240757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:50.958286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:55.680458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:59.315310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:03.125220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:07.957774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:11.523996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:15.386637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:19.834781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:24.057944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:27.709032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:32.236807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:36.265685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:27.305400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:31.817765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:35.378561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:39.096445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:43.547833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:47.437629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:51.161174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:55.876458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:15:59.520454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:03.330762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:08.153428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:11.709662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:15.586370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:20.054394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:24.272249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:27.890301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-07T17:16:32.448002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-07T17:17:03.713399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeAge RangeCostCurrencyKeyCustomerKeyDimCurrency.CurrencyNameDimCustomer.CommuteDistanceDimCustomer.EnglishEducationDimCustomer.EnglishOccupationDimCustomer.GenderDimCustomer.HouseOwnerFlagDimCustomer.MaritalStatusDimCustomer.NumberCarsOwnedDimCustomer.TotalChildrenDimCustomer.YearlyIncomeDimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryNameDimProduct.DimProductSubcategory.EnglishProductSubcategoryNameDimProduct.ProductSubcategoryKeyDimPromotion.EnglishPromotionCategoryDimPromotion.EnglishPromotionNameDimPromotion.EnglishPromotionTypeDimSalesTerritory.SalesTerritoryCountryDimSalesTerritory.SalesTerritoryGroupDimSalesTerritory.SalesTerritoryRegionExtendedAmountFreightProductKeyProductStandardCostProfitPromotionKeySalesAmountSalesOrderLineNumberSalesTerritoryKeyTaxAmtTotalProductCostUnitPriceYearlyIncomeRange
Age1.0000.916-0.0330.068-0.0280.0450.1920.2050.2420.0270.1280.1960.1930.5490.1840.0550.0310.0280.0070.0000.0030.0590.0820.046-0.032-0.032-0.012-0.033-0.0280.000-0.0320.011-0.072-0.032-0.033-0.0320.148
Age Range0.9161.0000.0030.046-0.0150.0490.0670.1030.2420.0000.1200.1630.0690.4520.2120.0030.025-0.0060.0000.0040.0000.0490.0450.0500.0030.003-0.0160.0030.0040.0040.0030.011-0.0360.0030.0030.0030.223
Cost-0.0330.0031.000-0.054-0.0270.1250.0440.0390.0620.0050.0250.0440.064-0.0520.0410.7070.480-0.7140.1870.1330.1500.0790.1120.0590.9980.998-0.1731.0000.9320.1330.998-0.3860.0580.9981.0000.9980.091
CurrencyKey0.0680.046-0.0541.0000.0961.0000.1340.1010.1170.0110.0510.0450.1080.077-0.1000.1170.0940.0490.0100.0060.0090.6900.7350.690-0.053-0.0530.014-0.054-0.0500.006-0.0530.037-0.651-0.053-0.054-0.0530.148
CustomerKey-0.028-0.015-0.0270.0961.0000.0920.0520.0510.0770.0230.1310.0930.044-0.025-0.0870.0640.1230.0160.0270.0260.0300.1030.0980.078-0.029-0.0290.068-0.027-0.0350.026-0.029-0.009-0.037-0.029-0.027-0.0290.094
DimCurrency.CurrencyName0.0450.0490.1251.0000.0921.0000.1680.1040.1650.0130.0530.0450.1340.046-0.1830.1180.0880.0320.0140.0080.0110.7560.8060.756-0.045-0.0450.008-0.044-0.0450.008-0.0450.041-0.137-0.045-0.044-0.0450.204
DimCustomer.CommuteDistance0.1920.0670.0440.1340.0520.1681.0000.1670.2490.0180.2400.0620.2920.0710.2200.0400.0390.0330.0000.0010.0000.2040.2820.205-0.023-0.0230.000-0.024-0.0170.001-0.023-0.009-0.164-0.023-0.024-0.0230.225
DimCustomer.EnglishEducation0.2050.1030.0390.1010.0510.1040.1671.0000.2650.0140.1420.0970.312-0.006-0.2060.0470.0400.0560.0000.0000.0000.1350.1760.139-0.056-0.0560.018-0.057-0.0500.000-0.0560.005-0.025-0.056-0.057-0.0560.256
DimCustomer.EnglishOccupation0.2420.2420.0620.1170.0770.1650.2490.2651.0000.0240.1320.1750.231-0.0340.1590.0250.0460.0110.0070.0050.0050.2930.4110.296-0.005-0.0050.003-0.006-0.0030.005-0.005-0.008-0.225-0.005-0.006-0.0050.641
DimCustomer.Gender0.0270.0000.0050.0110.0230.0130.0180.0140.0241.0000.0160.0510.0210.002-0.0050.0080.0020.0080.0000.0000.0000.0120.0000.018-0.009-0.0090.004-0.009-0.0090.000-0.009-0.0000.001-0.009-0.009-0.0090.018
DimCustomer.HouseOwnerFlag0.1280.1200.0250.0510.1310.0530.2400.1420.1320.0161.0000.3110.1600.1990.0580.0000.029-0.0070.0000.0000.0000.0540.0540.0580.0080.008-0.0140.0080.0100.0000.0080.005-0.0440.0080.0080.0080.082
DimCustomer.MaritalStatus0.1960.1630.0440.0450.0930.0450.0620.0970.1750.0510.3111.0000.126-0.174-0.1060.0420.046-0.0370.0020.0050.0020.0650.0520.0670.0330.033-0.0030.0350.0290.0050.033-0.0040.0250.0330.0350.0330.116
DimCustomer.NumberCarsOwned0.1930.0690.0640.1080.0440.1340.2920.3120.2310.0210.1600.1261.0000.2500.3980.0630.0550.0750.0000.0040.0070.1570.2090.159-0.059-0.059-0.001-0.061-0.0510.004-0.059-0.003-0.001-0.059-0.061-0.0590.312
DimCustomer.TotalChildren0.5490.452-0.0520.077-0.0250.0460.071-0.006-0.0340.0020.199-0.1740.2501.0000.2410.0660.0480.0550.0000.0050.0020.1080.1460.111-0.051-0.051-0.003-0.052-0.0470.005-0.0510.014-0.108-0.051-0.052-0.0510.175
DimCustomer.YearlyIncome0.1840.2120.041-0.100-0.087-0.1830.220-0.2060.159-0.0050.058-0.1060.3980.2411.0000.0250.035-0.0340.0110.0060.0080.3020.4300.2300.0430.043-0.0510.0410.0460.0060.0430.013-0.1230.0430.0410.0431.000
DimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryName0.0550.0030.7070.1170.0640.1180.0400.0470.0250.0080.0000.0420.0630.0660.0251.0001.000-0.7830.1140.0820.0830.1030.0880.1040.5850.585-0.2080.6050.3780.0820.585-0.1820.0420.5850.6050.5850.019
DimProduct.DimProductSubcategory.EnglishProductSubcategoryName0.0310.0250.4800.0940.1230.0880.0390.0400.0460.0020.0290.0460.0550.0480.0351.0001.0000.3260.1900.1300.1600.0840.1190.0630.0650.0650.5400.0610.1220.1300.065-0.3300.0130.0650.0610.0650.052
DimProduct.ProductSubcategoryKey0.028-0.006-0.7140.0490.0160.0320.0330.0560.0110.008-0.007-0.0370.0750.055-0.034-0.7830.3261.0000.1540.0900.1110.0710.0960.066-0.706-0.7060.310-0.714-0.5890.090-0.7060.295-0.062-0.706-0.714-0.7060.032
DimPromotion.EnglishPromotionCategory0.0070.0000.1870.0100.0270.0140.0000.0000.0070.0000.0000.0020.0000.0000.0110.1140.1900.1541.0001.0001.0000.0060.0080.0040.0030.003-0.0260.0020.0121.0000.003-0.0200.0100.0030.0020.0030.007
DimPromotion.EnglishPromotionName0.0000.0040.1330.0060.0260.0080.0010.0000.0050.0000.0000.0050.0040.0050.0060.0820.1300.0901.0001.0001.0000.0070.0080.0010.0030.003-0.0260.0020.0121.0000.003-0.0190.0100.0030.0020.0030.005
DimPromotion.EnglishPromotionType0.0030.0000.1500.0090.0300.0110.0000.0000.0050.0000.0000.0020.0070.0020.0080.0830.1600.1111.0001.0001.0000.0090.0080.006-0.004-0.004-0.035-0.0050.0061.000-0.004-0.0140.011-0.004-0.005-0.0040.004
DimSalesTerritory.SalesTerritoryCountry0.0590.0490.0790.6900.1030.7560.2040.1350.2930.0120.0540.0650.1570.1080.3020.1030.0840.0710.0060.0070.0091.0001.0001.000-0.038-0.0380.007-0.040-0.0330.007-0.0380.032-0.681-0.038-0.040-0.0380.329
DimSalesTerritory.SalesTerritoryGroup0.0820.0450.1120.7350.0980.8060.2820.1760.4110.0000.0540.0520.2090.1460.4300.0880.1190.0960.0080.0080.0081.0001.0001.0000.0490.049-0.0070.0480.0510.0080.049-0.040-0.0480.0490.0480.0490.400
DimSalesTerritory.SalesTerritoryRegion0.0460.0500.0590.6900.0780.7560.2050.1390.2960.0180.0580.0670.1590.1110.2300.1040.0630.0660.0040.0010.0061.0001.0001.000-0.032-0.0320.005-0.033-0.0300.001-0.0320.036-0.254-0.032-0.033-0.0320.330
ExtendedAmount-0.0320.0030.998-0.053-0.029-0.045-0.023-0.056-0.005-0.0090.0080.033-0.059-0.0510.0430.5850.065-0.7060.0030.003-0.004-0.0380.049-0.0321.0001.000-0.1630.9980.9480.1181.000-0.3930.0541.0000.9981.0000.090
Freight-0.0320.0030.998-0.053-0.029-0.045-0.023-0.056-0.005-0.0090.0080.033-0.059-0.0510.0430.5850.065-0.7060.0030.003-0.004-0.0380.049-0.0321.0001.000-0.1630.9980.9480.1181.000-0.3930.0541.0000.9981.0000.090
ProductKey-0.012-0.016-0.1730.0140.0680.0080.0000.0180.0030.004-0.014-0.003-0.001-0.003-0.051-0.2080.5400.310-0.026-0.026-0.0350.007-0.0070.005-0.163-0.1631.000-0.173-0.1010.132-0.163-0.255-0.012-0.163-0.173-0.1630.061
ProductStandardCost-0.0330.0031.000-0.054-0.027-0.044-0.024-0.057-0.006-0.0090.0080.035-0.061-0.0520.0410.6050.061-0.7140.0020.002-0.005-0.0400.048-0.0330.9980.998-0.1731.0000.9320.1330.998-0.3860.0580.9981.0000.9980.091
Profit-0.0280.0040.932-0.050-0.035-0.045-0.017-0.050-0.003-0.0090.0100.029-0.051-0.0470.0460.3780.122-0.5890.0120.0120.006-0.0330.051-0.0300.9480.948-0.1010.9321.0000.1220.948-0.4330.0440.9480.9320.9480.090
PromotionKey0.0000.0040.1330.0060.0260.0080.0010.0000.0050.0000.0000.0050.0040.0050.0060.0820.1300.0901.0001.0001.0000.0070.0080.0010.1180.1180.1320.1330.1221.0000.003-0.0200.0100.0030.0020.0030.005
SalesAmount-0.0320.0030.998-0.053-0.029-0.045-0.023-0.056-0.005-0.0090.0080.033-0.059-0.0510.0430.5850.065-0.7060.0030.003-0.004-0.0380.049-0.0321.0001.000-0.1630.9980.9480.0031.000-0.3930.0541.0000.9981.0000.090
SalesOrderLineNumber0.0110.011-0.3860.037-0.0090.041-0.0090.005-0.008-0.0000.005-0.004-0.0030.0140.013-0.182-0.3300.295-0.020-0.019-0.0140.032-0.0400.036-0.393-0.393-0.255-0.386-0.433-0.020-0.3931.000-0.014-0.393-0.386-0.3930.012
SalesTerritoryKey-0.072-0.0360.058-0.651-0.037-0.137-0.164-0.025-0.2250.001-0.0440.025-0.001-0.108-0.1230.0420.013-0.0620.0100.0100.011-0.681-0.048-0.2540.0540.054-0.0120.0580.0440.0100.054-0.0141.0000.0540.0580.0540.330
TaxAmt-0.0320.0030.998-0.053-0.029-0.045-0.023-0.056-0.005-0.0090.0080.033-0.059-0.0510.0430.5850.065-0.7060.0030.003-0.004-0.0380.049-0.0321.0001.000-0.1630.9980.9480.0031.000-0.3930.0541.0000.9981.0000.090
TotalProductCost-0.0330.0031.000-0.054-0.027-0.044-0.024-0.057-0.006-0.0090.0080.035-0.061-0.0520.0410.6050.061-0.7140.0020.002-0.005-0.0400.048-0.0330.9980.998-0.1731.0000.9320.0020.998-0.3860.0580.9981.0000.9980.091
UnitPrice-0.0320.0030.998-0.053-0.029-0.045-0.023-0.056-0.005-0.0090.0080.033-0.059-0.0510.0430.5850.065-0.7060.0030.003-0.004-0.0380.049-0.0321.0001.000-0.1630.9980.9480.0031.000-0.3930.0541.0000.9981.0000.090
YearlyIncomeRange0.1480.2230.0910.1480.0940.2040.2250.2560.6410.0180.0820.1160.3120.1751.0000.0190.0520.0320.0070.0050.0040.3290.4000.3300.0900.0900.0610.0910.0900.0050.0900.0120.3300.0900.0910.0901.000

Missing values

2024-04-07T17:16:36.748999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T17:16:37.910236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ProductKeyCustomerKeyPromotionKeyCurrencyKeySalesTerritoryKeySalesOrderNumberSalesOrderLineNumberRevisionNumberOrderQuantityUnitPriceExtendedAmountProductStandardCostTotalProductCostSalesAmountTaxAmtFreightOrderDateDueDateShipDateDimCurrency.CurrencyNameCustomerFullNameDimCustomer.BirthDateDimCustomer.MaritalStatusDimCustomer.GenderDimCustomer.YearlyIncomeDimCustomer.TotalChildrenDimCustomer.EnglishEducationDimCustomer.EnglishOccupationDimCustomer.HouseOwnerFlagDimCustomer.NumberCarsOwnedDimCustomer.DateFirstPurchaseDimCustomer.CommuteDistanceDimProduct.ProductSubcategoryKeyDimProduct.EnglishProductNameDimProduct.DimProductSubcategory.EnglishProductSubcategoryNameDimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryNameDimPromotion.EnglishPromotionNameDimPromotion.EnglishPromotionTypeDimPromotion.EnglishPromotionCategoryDimSalesTerritory.SalesTerritoryRegionDimSalesTerritory.SalesTerritoryCountryDimSalesTerritory.SalesTerritoryGroupCostProfitYearlyIncomeRangeAgeAge Range
03361450111004SO43700111699.0982699.0982413.1463413.1463699.098255.927917.47752010-12-292011-01-102011-01-05US DollarLucas Hill1943-11-10MM800004Graduate DegreeManagement122010-12-291-2 Miles2Road-650 Black, 62Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America486.5517212.5465$80,000 - $170,00080Over 45
13122761211004SO437191113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-01-032011-01-152011-01-10US DollarKelvin Huang1978-11-18MM600002High SchoolProfessional122011-01-035-10 Miles2Road-150 Red, 48Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$50,000 - $79,9994531-45
23321456011004SO43726111699.0982699.0982413.1463413.1463699.098255.927917.47752011-01-052011-01-172011-01-12US DollarCourtney Carter1976-03-31MM500001BachelorsSkilled Manual112011-01-052-5 Miles2Road-650 Black, 58Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America486.5517212.5465$50,000 - $79,99948Over 45
33142757811004SO437621113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-01-142011-01-262011-01-21US DollarAshley Washington1964-05-07MF600001BachelorsProfessional112011-01-142-5 Miles2Road-150 Red, 56Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$50,000 - $79,99959Over 45
43301455911004SO43771111699.0982699.0982413.1463413.1463699.098255.927917.47752011-01-162011-01-282011-01-23US DollarJeremy Murphy1976-03-20MF500001BachelorsSkilled Manual102011-01-162-5 Miles2Road-650 Red, 52Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America486.5517212.5465$50,000 - $79,99948Over 45
53122765111004SO438201113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-01-252011-02-062011-02-01US DollarPhilip Gomez1961-05-05MM900002High SchoolProfessional112011-01-2510+ Miles2Road-150 Red, 48Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$80,000 - $170,00062Over 45
63122803811004SO439301113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-02-012011-02-132011-02-08US DollarMelanie Foster1953-08-19MM700002Partial CollegeProfessional122011-02-0110+ Miles2Road-150 Red, 48Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$50,000 - $79,99970Over 45
73361457211004SO44002111699.0982699.0982413.1463413.1463699.098255.927917.47752011-02-162011-02-282011-02-23US DollarCharles Moore1976-10-13MF700003Graduate DegreeProfessional102011-02-162-5 Miles2Road-650 Black, 62Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America486.5517212.5465$50,000 - $79,99947Over 45
83102817211004SO441481113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-03-032011-03-152011-03-10US DollarJacqueline Ward1972-11-23MM600001Partial CollegeSkilled Manual112011-03-032-5 Miles2Road-150 Red, 62Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$50,000 - $79,99951Over 45
93122816811004SO441531113578.27003578.27002171.29422171.29423578.2700286.261689.45682011-03-042011-03-162011-03-11US DollarFernando Turner1967-04-09MF600001Partial CollegeSkilled Manual112011-03-042-5 Miles2Road-150 Red, 48Road BikesBikesNo DiscountNo DiscountNo DiscountSouthwestUnited StatesNorth America2547.01261031.2574$50,000 - $79,99956Over 45
ProductKeyCustomerKeyPromotionKeyCurrencyKeySalesTerritoryKeySalesOrderNumberSalesOrderLineNumberRevisionNumberOrderQuantityUnitPriceExtendedAmountProductStandardCostTotalProductCostSalesAmountTaxAmtFreightOrderDateDueDateShipDateDimCurrency.CurrencyNameCustomerFullNameDimCustomer.BirthDateDimCustomer.MaritalStatusDimCustomer.GenderDimCustomer.YearlyIncomeDimCustomer.TotalChildrenDimCustomer.EnglishEducationDimCustomer.EnglishOccupationDimCustomer.HouseOwnerFlagDimCustomer.NumberCarsOwnedDimCustomer.DateFirstPurchaseDimCustomer.CommuteDistanceDimProduct.ProductSubcategoryKeyDimProduct.EnglishProductNameDimProduct.DimProductSubcategory.EnglishProductSubcategoryNameDimProduct.DimProductSubcategory.DimProductCategory.EnglishProductCategoryNameDimPromotion.EnglishPromotionNameDimPromotion.EnglishPromotionTypeDimPromotion.EnglishPromotionCategoryDimSalesTerritory.SalesTerritoryRegionDimSalesTerritory.SalesTerritoryCountryDimSalesTerritory.SalesTerritoryGroupCostProfitYearlyIncomeRangeAgeAge Range
603885301497419810SO736092114.994.991.86631.86634.990.39920.12482013-12-212014-01-022013-12-28United Kingdom PoundK. Saravan1984-12-29SM300003Partial CollegeClerical122013-03-310-1 Miles37Touring Tire TubeTires and TubesAccessoriesNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope2.39032.5997$30,000 - $49,9993931-45
603892171497419810SO7360931134.9934.9913.086313.086334.992.79920.87482013-12-212014-01-022013-12-28United Kingdom PoundK. Saravan1984-12-29SM300003Partial CollegeClerical122013-03-310-1 Miles31Sport-100 Helmet, BlackHelmetsAccessoriesNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope16.760318.2297$30,000 - $49,9993931-45
603902311497419810SO7360941149.9949.9938.492338.492349.993.99921.24982013-12-212014-01-022013-12-28United Kingdom PoundJim Rodman1984-12-29SM300003Partial CollegeClerical122013-03-310-1 Miles21Long-Sleeve Logo Jersey, MJerseysClothingNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope43.74136.2487$30,000 - $49,9993931-45
603913612056219810SO736621112294.992294.991251.98131251.98132294.99183.599257.37482013-12-222014-01-032013-12-29United Kingdom PoundAndy Ruth1966-09-09MF1300003Partial CollegeProfessional132011-08-145-10 Miles1Mountain-200 Black, 42Mountain BikesBikesNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope1492.9553802.0347$80,000 - $170,00057Over 45
603925292270611008SO747481113.993.991.49231.49233.990.31920.09982014-01-162014-01-282014-01-23US DollarAndy Ruth1981-10-24SF300000Partial CollegeClerical112014-01-162-5 Miles37Road Tire TubeTires and TubesAccessoriesNo DiscountNo DiscountNo DiscountGermanyGermanyEurope1.91132.0787$30,000 - $49,9994231-45
603932252270611008SO747482118.998.996.92236.92238.990.71920.22482014-01-162014-01-282014-01-23US DollarAndy Ruth1981-10-24SF300000Partial CollegeClerical112014-01-162-5 Miles19AWC Logo CapCapsClothingNo DiscountNo DiscountNo DiscountGermanyGermanyEurope7.86631.1237$30,000 - $49,9994231-45
603944912270611008SO7474831153.9953.9941.572341.572353.994.31921.34982014-01-162014-01-282014-01-23US DollarTimothy Sneath1981-10-24SF300000Partial CollegeClerical112014-01-162-5 Miles21Short-Sleeve Classic Jersey, XLJerseysClothingNo DiscountNo DiscountNo DiscountGermanyGermanyEurope47.24136.7487$30,000 - $49,9994231-45
6039553517708110010SO7482811124.9924.999.34639.346324.991.99920.62482014-01-192014-01-312014-01-26US DollarTimothy Sneath1966-12-15MM200001Graduate DegreeClerical102013-11-130-1 Miles37LL Mountain TireTires and TubesAccessoriesNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope11.970313.0197$10,000 - $29,99957Over 45
6039647717708110010SO748282114.994.991.86631.86634.990.39920.12482014-01-192014-01-312014-01-26US DollarTimothy Sneath1966-12-15MM200001Graduate DegreeClerical102013-11-130-1 Miles28Water Bottle - 30 oz.Bottles and CagesAccessoriesNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope2.39032.5997$10,000 - $29,99957Over 45
6039747117708110010SO7482831163.5063.5023.749023.749063.505.08001.58752014-01-192014-01-312014-01-26US DollarNaN1966-12-15MM200001Graduate DegreeClerical102013-11-130-1 Miles25Classic Vest, SVestsClothingNo DiscountNo DiscountNo DiscountUnited KingdomUnited KingdomEurope30.416533.0835$10,000 - $29,99957Over 45